import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					KandinskyVaaPriorPipeline
 __A			       =					['''prompt''']
 __A			       =					['''prompt''', '''negative_prompt''']
 __A			       =					[
     '''num_images_per_prompt''',
     '''generator''',
     '''num_inference_steps''',
     '''latents''',
     '''negative_prompt''',
     '''guidance_scale''',
     '''output_type''',
     '''return_dict''',
 ]
 __A			       =					False







 @property
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Optional[Any]:


   """simple docstring"""







   return 32







 @property
 def 		__UpperCAmelCase				(		self				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   return 32







 @property
 def 		__UpperCAmelCase				(		self				:   Dict)						->	Dict:


   """simple docstring"""







   return self.time_input_dim







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Optional[int]:


   """simple docstring"""







   return self.time_input_dim * 4







 @property
 def 		__UpperCAmelCase				(		self				:   Any)						->	List[Any]:


   """simple docstring"""







   return 100







 @property
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
   return tokenizer







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	Any:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    CLIPTextConfig(
       bos_token_id=0							, eos_token_id=2							, hidden_size=self.text_embedder_hidden_size							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, layer_norm_eps=1e-0_5							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1000							, )
   return CLIPTextModelWithProjection(lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	int:


   """simple docstring"""







   torch.manual_seed(0)

   _UpperCamelCase           =    {
       "num_attention_heads": 2,
       "attention_head_dim": 12,
       "embedding_dim": self.text_embedder_hidden_size,
       "num_layers": 1,
   }

   _UpperCamelCase           =    PriorTransformer(**lowercase_)
   # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
   _UpperCamelCase           =    nn.Parameter(torch.ones(model.clip_std.shape))
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   str)						->	Union[str, Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    CLIPVisionConfig(
       hidden_size=self.text_embedder_hidden_size							, image_size=224							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, num_attention_heads=4							, num_channels=3							, num_hidden_layers=5							, patch_size=14							, )

   _UpperCamelCase           =    CLIPVisionModelWithProjection(lowercase_)
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   Any)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    CLIPImageProcessor(
       crop_size=224							, do_center_crop=lowercase_							, do_normalize=lowercase_							, do_resize=lowercase_							, image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							, image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							, resample=3							, size=224							, )

   return image_processor







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    self.dummy_prior
   _UpperCamelCase           =    self.dummy_image_encoder
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    self.dummy_tokenizer
   _UpperCamelCase           =    self.dummy_image_processor

   _UpperCamelCase           =    UnCLIPScheduler(
       variance_type="fixed_small_log"							, prediction_type="sample"							, num_train_timesteps=1000							, clip_sample=lowercase_							, clip_sample_range=10.0							, )

   _UpperCamelCase           =    {
       "prior": prior,
       "image_encoder": image_encoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "scheduler": scheduler,
       "image_processor": image_processor,
   }

   return components







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str]=0)						->	int:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "prompt": "horse",
       "generator": generator,
       "guidance_scale": 4.0,
       "num_inference_steps": 2,
       "output_type": "np",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()

   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   _UpperCamelCase           =    pipe.to(lowercase_)

   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    pipe(**self.get_dummy_inputs(lowercase_))
   _UpperCamelCase           =    output.image_embeds

   _UpperCamelCase           =    pipe(
       **self.get_dummy_inputs(lowercase_)							, return_dict=lowercase_							, )[0]

   _UpperCamelCase           =    image[0, -10:]
   _UpperCamelCase           =    image_from_tuple[0, -10:]

   assert image.shape == (1, 32)

   _UpperCamelCase           =    np.array(
       [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







 @skip_mps
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    torch_device == "cpu"
   _UpperCamelCase           =    True
   _UpperCamelCase           =    False

   self._test_inference_batch_single_identical(
       test_max_difference=lowercase_							, relax_max_difference=lowercase_							, test_mean_pixel_difference=lowercase_							, )







 @skip_mps
 def 		__UpperCAmelCase				(		self				:   List[str])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    torch_device == "cpu"
   _UpperCamelCase           =    False

   self._test_attention_slicing_forward_pass(
       test_max_difference=lowercase_							, test_mean_pixel_difference=lowercase_							, )





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    fname.split(os.path.sep )[-1]
  return re.search(r"^(.*)_\d+\.jpg$"    ,     a__ ).groups()[0]




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   int							, lowercase_				:   Dict=None							, lowercase_				:   Optional[Any]=None)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    file_names
   _UpperCamelCase           =    image_transform
   _UpperCamelCase           =    label_to_id







 def __len__(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   return len(self.file_names)







 def __getitem__(		self				:   int							, lowercase_				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.file_names[idx]
   _UpperCamelCase           =    PIL.Image.open(lowercase_)
   _UpperCamelCase           =    raw_image.convert("RGB")
   if self.image_transform is not None:
     _UpperCamelCase           =    self.image_transform(lowercase_)
   _UpperCamelCase           =    extract_label(lowercase_)
   if self.label_to_id is not None:
     _UpperCamelCase           =    self.label_to_id[label]
   return {"image": image, "label": label}


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.with_tracking:
    _UpperCamelCase           =    Accelerator(
        cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with="all"    ,     project_dir=args.project_dir )
  else:
    _UpperCamelCase           =    Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  _UpperCamelCase           =    config["image_size"]
  if not isinstance(a__    ,     (list, tuple) ):
    _UpperCamelCase           =    (image_size, image_size)

  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps    ,     "isdigit" ):
    if args.checkpointing_steps == "epoch":
      _UpperCamelCase           =    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _UpperCamelCase           =    int(args.checkpointing_steps )
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
  else:
    _UpperCamelCase           =    None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _UpperCamelCase           =    os.path.split(a__ )[-1].split("." )[0]
    accelerator.init_trackers(a__    ,     a__ )

  # Grab all the image filenames
  _UpperCamelCase           =    [os.path.join(args.data_dir    ,     a__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

  # Build the label correspondences
  _UpperCamelCase           =    [extract_label(a__ ) for fname in file_names]
  _UpperCamelCase           =    list(set(a__ ) )
  id_to_label.sort()
  _UpperCamelCase           =    {lbl: i for i, lbl in enumerate(a__ )}

  # Set the seed before splitting the data.
  np.random.seed(a__ )
  torch.manual_seed(a__ )
  torch.cuda.manual_seed_all(a__ )

  # Split our filenames between train and validation
  _UpperCamelCase           =    np.random.permutation(len(a__ ) )
  _UpperCamelCase           =    int(0.8 * len(a__ ) )
  _UpperCamelCase           =    random_perm[:cut]
  _UpperCamelCase           =    random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCamelCase           =    Compose([RandomResizedCrop(a__    ,     scale=(0.5, 1.0) ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset(
      [file_names[i] for i in train_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # For evaluation, we use a deterministic Resize
  _UpperCamelCase           =    Compose([Resize(a__ ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset([file_names[i] for i in eval_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    create_model("resnet50d"    ,     pretrained=a__    ,     num_classes=len(a__ ) )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _UpperCamelCase           =    model.to(accelerator.device )

  # Freezing the base model
  for param in model.parameters():
    _UpperCamelCase           =    False
  for param in model.get_classifier().parameters():
    _UpperCamelCase           =    True

  # We normalize the batches of images to be a bit faster.
  _UpperCamelCase           =    torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
  _UpperCamelCase           =    torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

  # Instantiate optimizer
  _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()    ,     lr=lr / 25 )

  # Instantiate learning rate scheduler
  _UpperCamelCase           =    OneCycleLR(optimizer=a__    ,     max_lr=a__    ,     epochs=a__    ,     steps_per_epoch=len(a__ ) )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )
  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase           =    0
  # We also need to keep track of the starting epoch so files are named properly
  _UpperCamelCase           =    0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
      accelerator.load_state(args.resume_from_checkpoint )
      _UpperCamelCase           =    os.path.basename(args.resume_from_checkpoint )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase           =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
      dirs.sort(key=os.path.getctime )
      _UpperCamelCase           =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase           =    os.path.splitext(a__ )[0]

    if "epoch" in training_difference:
      _UpperCamelCase           =    int(training_difference.replace("epoch_"    ,     "" ) ) + 1
      _UpperCamelCase           =    None
    else:
      _UpperCamelCase           =    int(training_difference.replace("step_"    ,     "" ) )
      _UpperCamelCase           =    resume_step // len(a__ )
      resume_step -= starting_epoch * len(a__ )

    # Now we train the model
  for epoch in range(a__    ,     a__ ):
    model.train()
    if args.with_tracking:
      _UpperCamelCase           =    0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _UpperCamelCase           =    accelerator.skip_first_batches(a__    ,     a__ )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _UpperCamelCase           =    train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    torch.nn.functional.cross_entropy(a__    ,     batch["label"] )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a__ )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(a__    ,     a__ ):
        _UpperCamelCase           =    f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
          accelerator.save_state(a__ )
    model.eval()
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      with torch.no_grad():
        _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    outputs.argmax(dim=-1 )
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather_for_metrics((predictions, batch["label"]) )
      _UpperCamelCase           =    predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _UpperCamelCase           =    accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 100 * eval_metric,
              "train_loss": total_loss.item() / len(a__ ),
              "epoch": epoch,
          }    ,     step=a__    ,     )
    if checkpointing_steps == "epoch":
      _UpperCamelCase           =    f'epoch_{epoch}'
      if args.output_dir is not None:
        _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
      accelerator.save_state(a__ )

  if args.with_tracking:
    accelerator.end_training()


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument("--data_dir"    ,     required=a__    ,     help="The data folder on disk." )
  parser.add_argument("--fp16"    ,     action="store_true"    ,     help="If passed, will use FP16 training." )
  parser.add_argument(
      "--mixed_precision"    ,     type=a__    ,     default=a__    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,     )
  parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU." )
  parser.add_argument(
      "--checkpointing_steps"    ,     type=a__    ,     default=a__    ,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,     )
  parser.add_argument(
      "--output_dir"    ,     type=a__    ,     default="."    ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,     )
  parser.add_argument(
      "--resume_from_checkpoint"    ,     type=a__    ,     default=a__    ,     help="If the training should continue from a checkpoint folder."    ,     )
  parser.add_argument(
      "--with_tracking"    ,     action="store_true"    ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,     )
  parser.add_argument(
      "--project_dir"    ,     type=a__    ,     default="logs"    ,     help="Location on where to store experiment tracking logs` and relevent project information"    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()




